Tackling Labor Market Duality and Informality
Annex 4.1 Drivers of Income Inequality
This annex presents the empirical framework and estimates of the drivers of income inequality. It builds on various studies in the empirical literature (Woo and others 2013; IMF 2014; Dabla-Norris and others 2015) to formulate the econometric strategy. The baseline model specification is as follows:
Inequalit = δXit–1 + µi + θt + εit
where Inequal denotes, for each country i and year t, a measure of income distribution, with the main measure being the net Gini.1 Xit–1 is the vector of explanatory variables and comprises human capital, technological progress, financial openness, trade openness, financial deepening, fiscal policy, inflation, and democratic accountability.2 The education variable, from the Penn World Table Version 8.1, captures the average years of schooling (Barro and Lee 2010). Technological progress is measured by the share of information technology capital in the total capital stock (Jorgenson and Vu 2007) and financial openness by the sum of assets and liabilities from the international investment position data over GDP. Trade openness is measured by the sum of exports and imports over GDP, financial deepening by domestic credit to the private sector as a share of GDP, fiscal policy by government consumption over GDP, and inflation by changes in the consumer price index (all from the World Economic Outlook). Democratic accountability (from the International Country Risk Guide data set) captures how responsive government is to its people.
µidenote the country-specific fixed effects to control for country-specific factors, including the time-invariant component of the institutional and geographical environments. θtare time-fixed effects to control for global factors, and εit is an error term. All explanatory variables in the estimation are lagged by one year to reduce the risks of endogeneity due to reverse causality.
To investigate whether the drivers of inequality in Asia differ from those in other regions, with a focus on policy variables, we augment our baseline specification
The main author of this annex is Tidiane Kinda.
1Our main results are robust with alternative measures of income inequality, such as market Gini, income share of the bottom 10 percent, income share of the top 10 percent, and the Palma ratio (See Jain-Chandra and others 2016).
2Our baseline regressions also control for income per capita and its squared term to test for the existence of Kuznets curves.
with various interaction terms by combining key policy variables with Asia dummies as illustrated below:
Inequalit = δXit–1 + γAsia * Zit–1 + µi + θt + εit where all variables are defined as above, and Zit–1 is the vector of policy variables and refers to human capital, financial deepening, and government consumption.
We further zoom in on each policy issue separately and use more granular data to assess the way in which that policy affects inequality in Asia. We focus on one policy area at a time to reduce the risk of collinearity while preserving an adequate number of variables and observations for each of our estimations.
The sample covers 82 advanced and developing economies, including 17 Asian countries, during the period 1990–2013. We rely mainly on fixed-effects (FE) panel regressions, with Driscoll-Kraay standard errors for our empirical investigation. The FE with Driscoll-Kraay standard errors are robust to very general forms of cross-sectional and temporal dependence. The error structure under this estimation method is assumed to be heteroscedastic and autocorrelated up to two lags, which helps capture the persistence of income inequality across time. The error is also assumed to be correlated between countries, possibly due to common shocks, for instance those related to technology, international trade, or financial crises.
The results from the baseline regressions are broadly in line with findings in the empirical literature. In particular, fiscal policy and technological progress seem to have been the two most important drivers of the net Gini for advanced economies, while financial deepening has been associated with rising inequality in developing countries (Annex Table 4.1.1).
Analyzing whether the drivers of income inequality in Asia differ from those in other regions highlights interesting findings. While financial deepening has been associated with higher inequality in other regions, it has been an equalizing force in Asia (Annex Table 4.1.2, column 1). Further investigating the specificity of Asia illustrates that limited and poorly targeted policies may have prevented Asian economies from benefiting in terms of equalizing expenditure policies. Indeed, unlike in other regions, education, social benefits, and capital spending seem to have been associated with higher income inequality in Asia (Annex Table 4.1.2, column 2). The contribution of skill premiums to higher inequality appears to have been three times larger in Asia than elsewhere (Annex Table 4.1.3, column 3).
Annex Table 4.1.1. Drivers of Income Inequality (Baseline)
Explanatory variables
Dependent variable: Net Gini Advanced economies
Developing economies
(1) (2)
Human Capital, t–1 –0.006 –0.048**
(–0.953) (–2.176)
Trade Openness, t–1 –0.010** –0.017**
(–2.536) (–2.055)
Financial Openness, t–1 –0.002 0.023
(–1.655) (1.643)
Financial Deepening, t–1 0.003 0.054***
(0.824) (4.289)
Technology, t–1 0.201* 0.158
(1.915) (1.135)
Gov. Consumption, t–1 –0.240*** –0.054
(–6.330) (–1.074)
Inflation, t–1 –0.039 –0.000
(–1.252) (–0.305) Democratic accountability, t–1 0.003 –0.003**
(1.512) (–2.412)
Observations 472 534
Number of countries 31 51
Time fixed effects YES YES
Source: IMF staff estimates.
Note: Driscoll-Kraay robust t-statistics in parentheses. They are robust to very general forms of cross-sectional and temporal dependence.
Log of GDP per capita and its squared term, as well as country fixed effects, time fixed effects and a constant term, are included in each regression but are not reported.
*** p<0.01, ** p<0.05, * p<0.1.
4. shARING ThE GROwTh dIvIdENd: ANALysIs Of INEqUALITy IN AsIA
Annex Table 4.1.2. Drivers of Income Inequality (Asian Specificity)
Explanatory variables Dependent variable: Net Gini
Asian Specificity Fiscal Policy Human Capital
Human Capital, t–1 -0.045***
(-5.983)
Human Capital*Asia, t–1 0.002
(0.078)
Financial Deepening, t–1 0.011***
(4.522) Financial Deepening*Asia, t–1 -0.015*
(-1.784)
Gov. Consumption, t–1 -0.199***
(-3.510)
Gov. Consumption*Asia, t–1 0.14
(1.210)
Top Corporate tax rate, t–1 -0.065***
(-3.464)
Top Personnal tax rate, t–1 -0.048
(-1.481)
Health Spending, t–1 0.244
(1.190)
Education Spending, t–1 -0.453**
(-2.472)
Social Benefits, t–1 -0.243***
(-6.810)
Capital Spending, t–1 -0.228***
(-2.909)
Top Corporate tax rate*Asia, t–1 -0.017
(-0.358)
Top Personal tax rate*Asia, t–1 0.015
(0.482)
Health Spending*Asia, t–1 -0.446
(-0.947)
Education Spending*Asia, t–1 0.943*
(1.968)
Social Benefits*Asia, t–1 0.680***
(3.890)
Capital Spending*Asia, t–1 0.399**
(2.642)
Skill Premium, t–1 0.007*
(1.982)
Skill Premium*Asia, t–1 0.022***
(2.998)
Primary school completion, t–1 -0.140***
(-4.139)
Primary school completion*Asia, t–1 0.141*
(1.787)
Secondary school enrollment, t–1 -0.006
(-0.180)
Secondary school enrollment*Asia, t–1 -0.074
(-0.948)
Tertiary school enrollment, t–1 0.090*
(1.989)
Tertiary school enrollment*Asia, t–1 -0.032
(-1.130)
Number of observations 848 519 232
Number of groups 78 56 42
Time fixed effects YES YES YES
Source: IMF staff estimates.
Note: Driscoll-Kraay robust t-statistics in parentheses. They are robust to very general forms of cross-sectional and temporal dependence. All regressions control for the determinants of inequality identified in the baseline specifications. Country fixed effects, time fixed effects, and a constant term are included in each regression but are not reported.
*** p<0.01, ** p<0.05, * p<0.1.
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